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Abundant mining and industrial activities located in the Upper Silesian Coal Basin lead to large emissions of the potent greenhouse gas methane. This study aims to quantify these emissions from continuous, high-precision airborne measurements and dispersion modeling. Our emission estimates are in line with E-PRTR 2017 reported values, albeit significantly lower (up to 40 %) than values reported in the EDGAR v4.3.2 inventory.
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Preprints
https://doi.org/10.5194/acp-2020-962
https://doi.org/10.5194/acp-2020-962

  24 Nov 2020

24 Nov 2020

Review status: this preprint is currently under review for the journal ACP.

Estimating Upper Silesian coal mine methane emissions from airborne in situ observations and dispersion modeling

Julian Kostinek1, Anke Roiger1, Maximilian Eckl1, Alina Fiehn1, Andreas Luther1, Norman Wildmann1, Theresa Klausner1, Andreas Fix1, Christoph Knote2, Andreas Stohl3, and André Butz4 Julian Kostinek et al.
  • 1Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
  • 2Meteorological Institute, Ludwig-Maximilians-University Munich, Munich, Germany
  • 3Department of Meteorology and Geophysics, University of Vienna, Vienna, Austria
  • 4Institute of Environmental Physics, University of Heidelberg, Heidelberg, Germany

Abstract. Abundant mining and industrial activities located in the Upper Silesian Coal Basin (USCB) lead to large emissions of the potent greenhouse gas (GHG) methane (CH4). The strong localization of CH4 emitters (mostly confined to known coal mine ventilation shafts) and the large emissions of 448/720 kt CH4 yr−1 reported in the European Pollutant Release and Transfer Register (E-PRTR 2017) and the Emissions Database for Global Atmospheric Research (EDGAR v4.3.2) make the USCB a prime research target for validating and improving CH4 flux estimation techniques. High-precision observations of this GHG were made downwind of local (e.g. single facilities) to regional-scale sources (e.g. agglomerations) in the context of the CoMet 1.0 campaign in early summer 2018. A Quantum Cascade/Interband Cascade Laser (QCL/ICL) based spectrometer adapted for airborne research was deployed aboard the German Aerospace Centers (DLR) Cessna 208B to sample the planetary boundary layer (PBL) in situ. Regional CH4 emission estimates for the USCB are derived using a model approach including assimilated wind soundings from three ground-based Doppler lidars. Although retrieving estimates for individual emitters is difficult using only single flights, due to sparse data availability, the combination of two flights allows for exploiting different meteorological conditions (analogous to a sparse tomography algorithm) to establish confidence on facility level estimates. Emission rates from individual sources are not only needed for unambiguous comparisons between bottom-up and top-down inventories but become indispensable if (independently verifiable) sanctions are to be imposed on individual companies emitting GHGs. An uncertainty analysis is presented for both the regional scale and facility level emission estimates. We find instantaneous emission estimates of 452/442 ± 78/75 kt CH4 yr−1 for the morning/afternoon flight of June 6th, 2018. The derived emission rates coincide (±2 %) with annual-average inventorial data from E-PRTR 2017 albeit they are distinctly lower (−37 %/−40 %) than values reported in EDGAR v4.3.2. Discrepancies in available emission inventories could potentially be narrowed down with sufficient observations using the method described herein to bridge the gap between instantaneous emission estimates and yearly averaged inventories.

Julian Kostinek et al.

 
Status: open (extended)
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Julian Kostinek et al.

Julian Kostinek et al.

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Short summary
Abundant mining and industrial activities located in the Upper Silesian Coal Basin lead to large emissions of the potent greenhouse gas methane. This study aims to quantify these emissions from continuous, high-precision airborne measurements and dispersion modeling. Our emission estimates are in line with E-PRTR 2017 reported values, albeit significantly lower (up to 40 %) than values reported in the EDGAR v4.3.2 inventory.
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